{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coupling GIPL and ECSimpleSnow models\n",
    "\n",
    "Before you begin, install:\n",
    "\n",
    "```conda install -c conda-forge pymt pymt_gipl pymt_ecsimplesnow seaborn```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[33;01m➡ models: FrostNumber, Ku, Hydrotrend, GIPL, ECSimpleSnow, Cem, Waves\u001b[39;49;00m\n"
     ]
    }
   ],
   "source": [
    "import pymt.models\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.colors as mcolors\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "sns.set(style='whitegrid', font_scale= 1.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load ECSimpleSnow module from PyMT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 1D Snow Model\n",
      "('snowpack__depth', 'snowpack__mass-per-volume_density')\n",
      "('precipitation_mass_flux', 'land_surface_air__temperature', 'precipitation_mass_flux_adjust_factor', 'snow_class', 'open_area_or_not', 'snowpack__initial_depth', 'snowpack__initial_mass-per-volume_density')\n"
     ]
    }
   ],
   "source": [
    "ec = pymt.models.ECSimpleSnow()\n",
    "print(ec.name)\n",
    "\n",
    "# List input and output variable names.\n",
    "print(ec.output_var_names)\n",
    "print(ec.input_var_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load GIPL module from PyMT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 1D GIPL Model\n",
      "('soil__temperature', 'model_soil_layer__count')\n",
      "('land_surface_air__temperature', 'snowpack__depth', 'snow__thermal_conductivity', 'soil_water__volume_fraction', 'soil_unfrozen_water__a', 'soil_unfrozen_water__b')\n"
     ]
    }
   ],
   "source": [
    "gipl = pymt.models.GIPL()\n",
    "print(gipl.name)\n",
    "\n",
    "# List input and output variable names.\n",
    "print(gipl.output_var_names)\n",
    "print(gipl.input_var_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the setup method on both ECSimpleSnow and GIPL to get default configuration files and data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('snow_model.cfg', '/Users/mpiper/projects/GIPL-BMI-Fortran/Notebooks')\n",
      "('gipl_config.cfg', '/Users/mpiper/projects/GIPL-BMI-Fortran/Notebooks')\n"
     ]
    }
   ],
   "source": [
    "ec_defaults = ec.setup('.')\n",
    "print(ec_defaults)\n",
    "\n",
    "gipl_defaults = gipl.setup('.')\n",
    "print(gipl_defaults)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ec.initialize('snow_model.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gipl.initialize('gipl_config.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get soil depth: [unit: m]\n",
    "depth = gipl.get_grid_z(2)\n",
    "n_depth = int(len(depth))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final soil temperatures will be  (176, 365)\n"
     ]
    }
   ],
   "source": [
    "# Get the length of forcing data:\n",
    "ntime = int(gipl.end_time)\n",
    "\n",
    "# Define a variable to store soil temperature through the time period\n",
    "\n",
    "tsoil = np.zeros((n_depth, ntime)) * np.nan\n",
    "\n",
    "print('Final soil temperatures will be ', tsoil.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[12,6])\n",
    "\n",
    "ax2 = fig.add_subplot(2,3,1)\n",
    "ax2.set_title('Air Temperature (Input)')\n",
    "\n",
    "ax3 = fig.add_subplot(2,3,2)\n",
    "ax3.set_title('Precipition (Input)')\n",
    "\n",
    "ax4 = fig.add_subplot(2,3,4)\n",
    "ax4.set_title('Snow Depth (EC Output)')\n",
    "\n",
    "ax5 = fig.add_subplot(2,3,5)\n",
    "ax5.set_title('Snow Density (EC Output)')\n",
    "\n",
    "ax1 = fig.add_subplot(2,3,(3,6))\n",
    "ax1.set_ylim([15,0])\n",
    "ax1.set_xlim([-20,20])\n",
    "ax1.set_xlabel('Soil Temperature ($^oC$)')\n",
    "ax1.set_ylabel('Depth (m)')\n",
    "ax1.plot([0,0],[15,0],'k--')\n",
    "\n",
    "for i in np.arange(365):\n",
    "    \n",
    "    ec.update() # Update Snow Model Once\n",
    "    \n",
    "    # Get output from snow model\n",
    "    tair  = ec.get_value('land_surface_air__temperature')\n",
    "    prec  = ec.get_value('precipitation_mass_flux')\n",
    "    snd   = ec.get_value('snowpack__depth', units='m')\n",
    "    rsn   = ec.get_value('snowpack__mass-per-volume_density', units = 'g cm-3')\n",
    "    \n",
    "    # Pass value to GIPL model\n",
    "    gipl.set_value('land_surface_air__temperature', tair)\n",
    "    gipl.set_value('snowpack__depth', snd)\n",
    "    gipl.set_value('snow__thermal_conductivity', rsn * rsn * 2.846)\n",
    "    \n",
    "    gipl.update() # Update GIPL model Once\n",
    "    \n",
    "    tsoil[:,i] = gipl.get_value('soil__temperature') # Save results to a matrix\n",
    "    \n",
    "    ax1.plot(tsoil[depth>=0,i], depth[depth>=0],color = [0.7,0.7,0.7], alpha = 0.1)\n",
    "    \n",
    "    ax2.scatter(i, tair, c = 'k')\n",
    "    ax3.scatter(i, prec, c = 'k')\n",
    "    ax4.scatter(i, snd , c = 'k')\n",
    "    ax5.scatter(i, rsn , c = 'k')\n",
    "    \n",
    "ax1.plot(tsoil[depth>=0,:].max(axis=1), depth[depth>=0], 'r', linewidth = 2, label = 'Max')\n",
    "ax1.plot(tsoil[depth>=0,:].min(axis=1), depth[depth>=0], 'b', linewidth = 2, label = 'Min')\n",
    "ax1.plot(tsoil[depth>=0,:].mean(axis=1), depth[depth>=0], 'k', linewidth = 2, label = 'Mean')\n",
    "ax1.legend()\n",
    "ax1.set_title('Ground Temperatures (GIPL output)')\n",
    "\n",
    "ax2.set_xticks([])\n",
    "ax3.set_xticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x1c1c9bfeb8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[9,4])\n",
    "divnorm = mcolors.DivergingNorm(vmin=-25., vcenter=0., vmax=10)\n",
    "plt.contourf(np.arange(ntime), depth, tsoil, np.linspace(-25,10,15), \n",
    "             norm = divnorm,\n",
    "             cmap=\"RdBu_r\", extend = 'both')\n",
    "\n",
    "plt.ylim([5,0])\n",
    "cb = plt.colorbar()\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Depth (m)')\n",
    "cb.ax.set_ylabel('Soil Temperature ($^oC$)')\n",
    "\n",
    "plt.contour(np.arange(ntime), depth, tsoil, [0]) # ZERO "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
